import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
# from scipy.special import params

from lr_utils import load_dataset

train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()

# index = 5
# plt.imshow(train_set_x_orig[index])
# plt.show()
# print ("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +  "' picture.")
m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0]
num_px = train_set_x_orig.shape[1]

# print ("Number of training examples: m_train = " + str(m_train))
# print ("Number of testing examples: m_test = " + str(m_test))
# print ("Height/Width of each image: num_px = " + str(num_px))
# print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
# print ("train_set_x shape: " + str(train_set_x_orig.shape))
# print ("train_set_y shape: " + str(train_set_y.shape))
# print ("test_set_x shape: " + str(test_set_x_orig.shape))
# print ("test_set_y shape: " + str(test_set_y.shape))

# 将图片铺平
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T

# print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))
# print ("train_set_y shape: " + str(train_set_y.shape))
# print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))
# print ("test_set_y shape: " + str(test_set_y.shape))
# print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))

# 对数据集进行居中和标准化，减去整个numpy数组的均值，然后除以整个numpy数组的标准差

train_set_x = train_set_x_flatten / 255
test_set_x = test_set_x_flatten / 255

# 3.学习算法一般架构
# z(x) = w^T * x + b
# a(z) = sigmoid(z) = 1 / (1 + e^(-z))
# ^y(i) = a(i) = sigmoid(z(i))
# L(a(i), y(i)) = -y(i)㏒(a(i)) - (1 - y(i))㏒(1 - a(i))
# J = 1 / m * ∑L(a(i), y(i))

# 4.构建算法
# 4.1 辅助算法
def sigmoid(z):
    s = 1 / (1 + np.exp(-z))
    return s
# print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))

# 4.2 初始化参数
def initialize_with_zeros(dim):
    w = np.zeros((dim, 1))
    b = 0
    assert (w.shape == (dim, 1))
    assert (isinstance(b, float) or isinstance(b, int))
    return w, b
# dim = 2
# w, b = initialize_with_zeros(dim)
# print ("w = " + str(w))
# print ("b = " + str(b))

# 4.3 向前和向后传播
    # 向前传播
        # 1. 得到X
        # 2. 计算 A = sigmoid(w ^ T * X + b) = {a(0), a(1), ......, a(m)}
        # 3. 计算损失函数 J = -1 / m * ∑[y(i)㏒(a(i)) - (1 - y(i))㏒(1 - a(i))]
        # 4. w` = 1 / m * X (A - Y) ^ T
        # 5. b` = 1 / m * ∑(a(i) - y(i))

"""
       计算损失函数和梯度

       参数:
       w -- 权重, 每个组各个x的权重
       b -- 偏置
       X -- 数据值
       Y -- 标签值

       Return:
       cost -- 损失函数值
       dw -- w的导数
       db -- b的导数
       """
def propagate(w, b, X, Y):

    m = X.shape[1]
    A = sigmoid(np.dot(w.T, X) + b)
    cost = -1 / m * np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))

    dw = 1 / m * np.dot(X, (A - Y).T)
    db = 1 / m * np.sum(A - Y)
    assert (dw.shape == w.shape)
    assert (db.dtype == float)
    cost = np.squeeze(cost)
    assert (cost.shape == ())

    grads = {"dw": dw,
             "db": db}

    return grads, cost
w, b, X, Y = np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]])
grads, cost = propagate(w, b, X, Y)
# print ("dw = " + str(grads["dw"]))
# print ("db = " + str(grads["db"]))
# print ("cost = " + str(cost))

# 4.4 优化函数
    # 初始化参数
    # 计算损失函数及其梯度
    # 使用梯度下降来更新参数
    # w = w - ɑ * w`
    # b = b - ɑ * b`
"""
    梯度下降算法优化w和b

    参数:
        w -- 权重, 每个组各个x的权重
        b -- 偏置
        X -- 数据值
        Y -- 标签值
        num_iterations -- 迭代次数
        learning_rate -- 学习率
        print_cost -- 迭代一百次打印一次

    Returns:
    params -- w和b
    grads -- w`和b`
    costs -- 迭代中所有损失函数值, 用来绘制学习曲线
    """
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False) :
    costs = []
    for i in range(num_iterations):
        grads, cost = propagate(w, b, X, Y)

        dw = grads["dw"]
        db = grads["db"]

        w = w - learning_rate * dw
        b = b - learning_rate * db

        if i % 100 == 0 :
            costs.append(cost)

        if print_cost and i % 100 == 0 :
            print("cost after iteration %i : %f" %(i, cost))

    params = {"w": w,
              "b": b}
    grads = {"dw": dw,
             "db": db}

    return params, grads, costs
params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)

# print ("w = " + str(params["w"]))
# print ("b = " + str(params["b"]))
# print ("dw = " + str(grads["dw"]))
# print ("db = " + str(grads["db"]))
# print(costs)

# predict()
    # 1. ^y = A = sigmoid(W ^ T * X + b)
    # 2. 如果^y > 0.5 计为1, 如果y^ < 0.5 计为0, 将结果存在Y_prediction
def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)

    A = sigmoid(np.dot(w.T, X) + b)
    for i in range(A.shape[1]) :
        if A[0, i] <= 0.5 :
            Y_prediction[0, i] = 0
        else:
            Y_prediction[0, i] = 1
    assert (Y_prediction.shape == (1,m))
    return Y_prediction
# print ("predictions = " + str(predict(w, b, X)))

# 5. 将功能合并到模型中
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    w, b = initialize_with_zeros(X_train.shape[0])

    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)

    w = parameters["w"]
    b = parameters["b"]

    Y_prediction_test = predict(w, b, X_test)
    Y_prediction_train = predict(w, b, X_train)

    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))

    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test,
         "Y_prediction_train": Y_prediction_train,
         "w": w,
         "b": b,
         "learning_rate": learning_rate,
         "num_iterations": num_iterations}

    return d
d = model(train_set_x, train_set_y, test_set_x, test_set_y, 3000, learning_rate = 0.005, print_cost = True)

# index = 1
# plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))
# plt.show()
# print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[int(d["Y_prediction_test"][0,index])].decode("utf-8") +  "\" picture.")

# costs = np.squeeze(d['costs'])
# plt.plot(costs)
# plt.ylabel('cost')
# plt.xlabel('iterations (per hundreds)')
# plt.title("Learning rate =" + str(d["learning_rate"]))
# plt.show()

# learning_rates = [0.01, 0.001, 0.0001]
# models = {}
# for i in learning_rates:
#     print ("learning rate is: " + str(i))
#     models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)
#     print ('\n' + "-------------------------------------------------------" + '\n')
#
# for i in learning_rates:
#     plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))
#
# plt.ylabel('cost')
# plt.xlabel('iterations')
#
# legend = plt.legend(loc='upper center', shadow=True)
# frame = legend.get_frame()
# frame.set_facecolor('0.90')
# plt.show()

# fname = './cat_in_iran.jpg'
# fname = './cat2.jpeg'
fname = 'other.jpg'
image = Image.open(fname)

num_px = 64
image = image.resize((num_px, num_px))
my_image = np.array(image)

my_image = my_image.reshape((1, num_px * num_px * 3)).T


# my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
my_predicted_image = predict(d["w"], d["b"], my_image)
print("y = " + str(np.squeeze(my_predicted_image)) + ", 你的算法预测是 \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" 图片")
plt.imshow(image)
plt.show()
